Bottom Line:
The ability to create accurate geometric models of neuronal morphology is important for understanding the role of shape in information processing.This paper describes Neuromantic, an open source system for three dimensional digital tracing of neurites.Practical considerations for reducing the computational time and space requirements of the extended algorithm are also discussed.

Affiliation: School of Systems Engineering, University of Reading Reading, UK.

ABSTRACTThe ability to create accurate geometric models of neuronal morphology is important for understanding the role of shape in information processing. Despite a significant amount of research on automating neuron reconstructions from image stacks obtained via microscopy, in practice most data are still collected manually. This paper describes Neuromantic, an open source system for three dimensional digital tracing of neurites. Neuromantic reconstructions are comparable in quality to those of existing commercial and freeware systems while balancing speed and accuracy of manual reconstruction. The combination of semi-automatic tracing, intuitive editing, and ability of visualizing large image stacks on standard computing platforms provides a versatile tool that can help address the reconstructions availability bottleneck. Practical considerations for reducing the computational time and space requirements of the extended algorithm are also discussed.

Figure 1: The process of semi-manual reconstruction and the Neuromantic application. The top panel illustrates the process of reconstruction from an image stack to a full 3D reconstruction, and the bottom panel displays the application interface with labels indicating the main features. Most of the functionality available via the interface is also replicated in mouse and keyboard shortcuts for efficiency.

Mentions:
Figure 1 shows a selection of screenshots from the current release of Neuromantic, illustrating the reconstruction process, from initial loading of the stack, through tracing the tree, culminating in a full 3D rendering of the finished reconstruction.

Figure 1: The process of semi-manual reconstruction and the Neuromantic application. The top panel illustrates the process of reconstruction from an image stack to a full 3D reconstruction, and the bottom panel displays the application interface with labels indicating the main features. Most of the functionality available via the interface is also replicated in mouse and keyboard shortcuts for efficiency.

Mentions:
Figure 1 shows a selection of screenshots from the current release of Neuromantic, illustrating the reconstruction process, from initial loading of the stack, through tracing the tree, culminating in a full 3D rendering of the finished reconstruction.

Bottom Line:
The ability to create accurate geometric models of neuronal morphology is important for understanding the role of shape in information processing.This paper describes Neuromantic, an open source system for three dimensional digital tracing of neurites.Practical considerations for reducing the computational time and space requirements of the extended algorithm are also discussed.

Affiliation:
School of Systems Engineering, University of Reading Reading, UK.

ABSTRACTThe ability to create accurate geometric models of neuronal morphology is important for understanding the role of shape in information processing. Despite a significant amount of research on automating neuron reconstructions from image stacks obtained via microscopy, in practice most data are still collected manually. This paper describes Neuromantic, an open source system for three dimensional digital tracing of neurites. Neuromantic reconstructions are comparable in quality to those of existing commercial and freeware systems while balancing speed and accuracy of manual reconstruction. The combination of semi-automatic tracing, intuitive editing, and ability of visualizing large image stacks on standard computing platforms provides a versatile tool that can help address the reconstructions availability bottleneck. Practical considerations for reducing the computational time and space requirements of the extended algorithm are also discussed.